def loaddata_old(dicomdata):
indata = pydicom_series.read_files(dicomdata,showProgress=True)[0]
pixelsize = float(indata.info["0028","0030"].value[0])
array = indata.get_pixel_array()
return pixelsize, array, indata
def __init__(self,path):
self.dcmseries = dicomseries.read_files(path,True,True)
self.tagdict = {'Protocol Name': ['0018', '1030'], 'Scan sequence': ['0018', '0020'], 'Acq Mat': ['0018', '1310'], 'Instance No': ['0020', '0013'], 'TR': ['0018', '0080'], 'TE': ['0018', '0081'], 'Image No': ['0051', '1008'],
'Series No': ['0020', '0011'], 'PixelSize':['0028','0030']}
self.datadict = {}
self.fill_datadict()
def loaddata(dicomdata):
indata = pydicom_series.read_files(dicomdata,showProgress=True)[0]
pixelsize = float(indata.info["0028","0030"].value[0])
rows = indata.info["0028","0010"].value
cols = indata.info["0028","0011"].value
newshape = (rows,cols)
print "shape of dataslices: ", newshape
bitdepth = indata.info["0028","0100"].value #bits allocated
if bitdepth == 16:
bv = np.int16
elif bitdepth == 8:
bv = np.int8
elif bitdepth == 32:
bv = np.int32
elif bitdepth == 64:
bv = np.int64
datlist = []
sequence = indata.__dict__['_datasets']
for i in range(len(sequence)):
tmpkeys = sequence[i].keys()
datlist.append(np.reshape(np.fromstring(sequence[i][dicom.tag.Tag("7fe0","0010")].value,dtype=bv),newshape))
array = np.array(datlist)
print "loaddata complete"
return pixelsize, array, indata
def sfnrTest(data,results,params):
"""
MRI_fBIRN Checks: fBIRN QA
Signal-to-Fluctuation-Noise Ratio (SFNR)
... (more tests to come eventually)
Workflow:
1. Read image or sequence
2. Run test
3. Build output
"""
#(1) Reads input file(s) list as a series (scan) single DICOM object
# returns DICOM header, raw pizelData object scaled and type of current DICOM object { 2D, 3D, ... }
#dcmInfile,pixeldataIn,dicomMode = wadwrapper_lib.prepareInput(data.series_filelist[0],headers_only=False,logTag=logTag())
#------------------------------------------------------------------
pixeldataIn = None
if ( len(data.series_filelist[0]) > 1 ) or ( len(data.series_filelist[0]) == 1 and os.path.isdir(data.series_filelist[0][0]) ):
fileList = data.series_filelist[0]
if len(data.series_filelist[0]) == 1 :
fileList = data.series_filelist[0][0]
# read/load a list of DICOM files
seriesDataList = pydicom_series.read_files(fileList,showProgress=True, readPixelData=True,skipNonImageFiles=True)
# check number of series in the array/list
if len(seriesDataList) != 1:
raise ValueError("{} Such test is supposed to apply solely to a single series/scan. Something went wrong...".format(logTag))
seriesData = seriesDataList[0]
nTemporalPositions = int(seriesData.info["0020","0105"].value)
# Image pixeldata seems to be transposed when read using wadwrapper_lib methods...
pixeldataIn = seriesData.get_pixel_array()
pixeldataIn = np.transpose(pixeldataIn)
new3rdDimension = int(pixeldataIn.shape[2])/nTemporalPositions
print '[Debug] Image dimensions: ' + str(pixeldataIn.shape)
print '[Debug] New dimensions: (%s, %s, %s, %s)' %(str(pixeldataIn.shape[0]),str(pixeldataIn.shape[1]),str(new3rdDimension),str(nTemporalPositions))
pixeldataIn = np.reshape(pixeldataIn, (pixeldataIn.shape[0],pixeldataIn.shape[1],new3rdDimension,nTemporalPositions))
elif ( len(data.series_filelist[0]) == 1) and ( wadwrapper_lib.testIfEnhancedDICOM(data.series_filelist[0][0]) ):
# read/load a single DICOM file
dcmInfile,pixeldataIn,dicomMode = wadwrapper_lib.prepareEnhancedInput(data.series_filelist[0][0],headers_only=False)
# DICOM keeps NumberOfTemporalPositions nested in sequence items/subitems. And DCM4CHEE seems not to keep them at all (!?).
# Workaround: Use Philips private tag, should be read as a bit string and converted to string/integer/whatever
if 'PHILIPS' not in (wadwrapper_lib.readDICOMtag("0008,0070",dcmInfile)).upper():
raise ValueError("{} Input enhanced dataset type not suitable --> no dynamics/temporal information found".format(logTag))
nSlicesRaw = wadwrapper_lib.readDICOMtag("2001,1018",dcmInfile)
try:
nSlices = struct.unpack("<L",nSlicesRaw)[0]
except:
nSlices = nSlicesRaw
nTemporalPositions = int(pixeldataIn.shape[0])/int(nSlices)
# Image pixeldata seems to be transposed when read using wadwrapper_lib methods...
pixeldataIn = np.transpose(pixeldataIn)
new3rdDimension = int(pixeldataIn.shape[2])/nTemporalPositions
print '[Debug] Image dimensions: ' + str(pixeldataIn.shape)
print '[Debug] New dimensions: (%s, %s, %s, %s)' %(str(pixeldataIn.shape[0]),str(pixeldataIn.shape[1]),str(new3rdDimension),str(nTemporalPositions))
pixeldataIn = np.reshape(pixeldataIn, (pixeldataIn.shape[0],pixeldataIn.shape[1],new3rdDimension,nTemporalPositions))
else:
raise ValueError("{} Input dataset type cannot be determined or is not compatible".format(logTag))
#(2)
#if 'BIRN' not in options['seriesDesc'] or 'BIRN' not in options['protocolName']:
# raise ValueError("{} Input dataset type not suitable".format(logTag))
# OR
# print '[Warning] Not an fBIRN scan --> do nothing'
#Check if pixeldataIn is actually a numpy array
if type(pixeldataIn).__module__ != np.__name__ :
raise ValueError("{} Unable to pull out the pixel data of the incomming DICOM file(s)".format(logTag))
output = fBIRN_lib.fBIRN_SFNR(pixeldataIn,plot_data=False)
#(3)
results.addFloat('mean_SNR', np.mean(output['imgsnr']), quantity='SNR', level=2) #quantity is actually magnitude in the WAD-QC app
results.addFloat('mean_SFNR', output['meansfnr'], quantity='SFNR', level=2) #quantity is actually magnitude in the WAD-QC app
#print '[info] SNR, %f'%np.mean(output['imgsnr'])
#print '[info] SFNR, %f'%output['meansfnr']
#print '[info] drift, %f'%output['trend'].params[1]
#if len(output['spikes']) > 0:
#print '[info] nspikes,%d'%len(output['spikes'])
if len(output['spikes']) > 0:
results.addBool('spikes', True, level=2) #Spikes detected
else :
results.addBool('spikes', False, level=2) #No spikes detected
def sfnrTest(data, results, params):
"""
MRI_fBIRN Checks: fBIRN QA
Signal-to-Fluctuation-Noise Ratio (SFNR)
... (more tests to come eventually)
Workflow:
1. Read image or sequence
2. Run test
3. Build output
"""
#(1) Reads input file(s) list as a series (scan) single DICOM object
# returns DICOM header, raw pizelData object scaled and type of current DICOM object { 2D, 3D, ... }
#dcmInfile,pixeldataIn,dicomMode = wadwrapper_lib.prepareInput(data.series_filelist[0],headers_only=False,logTag=logTag())
#------------------------------------------------------------------
pixeldataIn = None
if (len(data.series_filelist[0]) > 1) or (len(data.series_filelist[0]) == 1
and os.path.isdir(
data.series_filelist[0][0])):
fileList = data.series_filelist[0]
if len(data.series_filelist[0]) == 1:
fileList = data.series_filelist[0][0]
# read/load a list of DICOM files
seriesDataList = pydicom_series.read_files(fileList,
showProgress=True,
readPixelData=True,
skipNonImageFiles=True)
# check number of series in the array/list
if len(seriesDataList) != 1:
raise ValueError(
"{} Such test is supposed to apply solely to a single series/scan. Something went wrong..."
.format(logTag))
seriesData = seriesDataList[0]
nTemporalPositions = int(seriesData.info["0020", "0105"].value)
# Image pixeldata seems to be transposed when read using wadwrapper_lib methods...
pixeldataIn = seriesData.get_pixel_array()
pixeldataIn = np.transpose(pixeldataIn)
new3rdDimension = int(pixeldataIn.shape[2]) / nTemporalPositions
print '[Debug] Image dimensions: ' + str(pixeldataIn.shape)
print '[Debug] New dimensions: (%s, %s, %s, %s)' % (
str(pixeldataIn.shape[0]), str(pixeldataIn.shape[1]),
str(new3rdDimension), str(nTemporalPositions))
pixeldataIn = np.reshape(pixeldataIn,
(pixeldataIn.shape[0], pixeldataIn.shape[1],
new3rdDimension, nTemporalPositions))
elif (len(data.series_filelist[0])
== 1) and (wadwrapper_lib.testIfEnhancedDICOM(
data.series_filelist[0][0])):
# read/load a single DICOM file
dcmInfile, pixeldataIn, dicomMode = wadwrapper_lib.prepareEnhancedInput(
data.series_filelist[0][0], headers_only=False)
# DICOM keeps NumberOfTemporalPositions nested in sequence items/subitems. And DCM4CHEE seems not to keep them at all (!?).
# Workaround: Use Philips private tag, should be read as a bit string and converted to string/integer/whatever
if 'PHILIPS' not in (wadwrapper_lib.readDICOMtag(
"0008,0070", dcmInfile)).upper():
raise ValueError(
"{} Input enhanced dataset type not suitable --> no dynamics/temporal information found"
.format(logTag))
nSlicesRaw = wadwrapper_lib.readDICOMtag("2001,1018", dcmInfile)
try:
nSlices = struct.unpack("<L", nSlicesRaw)[0]
except:
nSlices = nSlicesRaw
nTemporalPositions = int(pixeldataIn.shape[0]) / int(nSlices)
# Image pixeldata seems to be transposed when read using wadwrapper_lib methods...
pixeldataIn = np.transpose(pixeldataIn)
new3rdDimension = int(pixeldataIn.shape[2]) / nTemporalPositions
print '[Debug] Image dimensions: ' + str(pixeldataIn.shape)
print '[Debug] New dimensions: (%s, %s, %s, %s)' % (
str(pixeldataIn.shape[0]), str(pixeldataIn.shape[1]),
str(new3rdDimension), str(nTemporalPositions))
pixeldataIn = np.reshape(pixeldataIn,
(pixeldataIn.shape[0], pixeldataIn.shape[1],
new3rdDimension, nTemporalPositions))
else:
raise ValueError(
"{} Input dataset type cannot be determined or is not compatible".
format(logTag))
#(2)
#if 'BIRN' not in options['seriesDesc'] or 'BIRN' not in options['protocolName']:
# raise ValueError("{} Input dataset type not suitable".format(logTag))
# OR
# print '[Warning] Not an fBIRN scan --> do nothing'
#Check if pixeldataIn is actually a numpy array
if type(pixeldataIn).__module__ != np.__name__:
raise ValueError(
"{} Unable to pull out the pixel data of the incomming DICOM file(s)"
.format(logTag))
output = fBIRN_lib.fBIRN_SFNR(pixeldataIn, plot_data=False)
#(3)
results.addFloat(
'mean_SNR', np.mean(output['imgsnr']), quantity='SNR',
level=2) #quantity is actually magnitude in the WAD-QC app
results.addFloat(
'mean_SFNR', output['meansfnr'], quantity='SFNR',
level=2) #quantity is actually magnitude in the WAD-QC app
#print '[info] SNR, %f'%np.mean(output['imgsnr'])
#print '[info] SFNR, %f'%output['meansfnr']
#print '[info] drift, %f'%output['trend'].params[1]
#if len(output['spikes']) > 0:
#print '[info] nspikes,%d'%len(output['spikes'])
if len(output['spikes']) > 0:
results.addBool('spikes', True, level=2) #Spikes detected
else:
results.addBool('spikes', False, level=2) #No spikes detected
